Scheduling periodic sensors for instantaneous aggregated traffic minimization

In IoT paradigm, Sensor-Cloud Infrastructure provides sensor nodes that sense various environmental parameters, generates the data and sends the same to the desired destination, say a cloud server through a common gateway. Sensor nodes with different data streaming specifications, can generate huge amount of traffic, if streams data simultaneously towards the destination, according to a random schedule. This can lead to higher bandwidth requirements in the wireless medium and increase the amount of data to be received at the gateway in any time slot. This further increases the channel capacity required at the access link to transmit the received data from gateway to the server. An optimal schedule of the sensor nodes will lead to minimization of instantaneous aggregated traffic in both the wireless medium and the access link. Thus leading to minimization of required bandwidth at the wireless medium and channel capacity at the access link. This would further increase the resource utilization of minimize the service provisioning cost of the sensor-cloud infrastructure. A straight forward optimization of the problem of minimizing the instantaneous aggregated traffic load generated from n sensor nodes require an exponential time to find the optimal schedule. Thus, in this paper, an ILP formulation and a polynomial-time heuristic algorithm is presented.